Gray Matter Differentiation Between Two Subordinate Personality Traits of Extraversion: Enthusiasm and Assertiveness

Enthusiasm and assertiveness are two subordinate personality traits of extraversion. These traits reflect different aspects of extroversion and have distinct implications on mental health. Whereas enthusiasm predicts satisfaction in life and positive relationships, assertiveness predicts psychological distress and reduced social support. The neural basis of these subordinate traits is not well understood. To investigate brain regions where enthusiasm and assertiveness have diverging relationship with morphology, enthusiasm and assertiveness were regressed to gray matter volume (GMV) across the whole brain in a sample of 301 healthy individuals. A significant interaction was found between enthusiasm and assertiveness in the left angular gyrus (t(296) = 4.18, family wise error corrected, FWE p = .001 (cluster-level); Cluster size = 880 voxels). Larger GMV in this area was associated with more enthusiasm and less assertiveness. Our study emphasizes the value of separating extraversion into its subordinate traits when investigating associations to neuroanatomy.

Application of the K-Means algorithm to determine poverty status in Hulu Sungai Tengah

Poverty is a condition of living in an inability to meet the minimum needs of life or basic needs. In Indonesia, poverty is one of the main problems that still need an optimal solution. Several government programs to address the problem of poverty have been carried out, but not infrequently the implementation is not right on target. The importance of this assistance is expected to improve the welfare of the community so it is very unfortunate if the assistance has not been right on target. This study aims to determine the status of poverty in Hulu Sungai Tengah Regency. By observing a problem above, it can be necessary to use a grouping method in determining poverty status. so that in this study using the cluster method, namely K-Means in clustering population data. Based on the results of data analysis using 353 head of family in the population data of HST Regency, it can be concluded that there are three poverty status clusters, namely low-level poverty (cluster 3) with a total of 130 head of family, medium-level poverty (cluster 2) with a total of 130 head of family. 111 head of family, and high poverty level (cluster 1) with a total of 112 head of family.

Clustering Clustering Data Eskspor Buah-Buahan Berdasarkan Negera Tujuan Menggunakan Algoritma K-Means

Abstrak: Ekspor merupakan kegiatan ekonomi dalam memasarkan dan jual barang, baik industry, pangan, serta kebutuhan lainnya kepada negara lainnya yang memiliki kurs atau nilai mata uang asing yang lebih besar, tujuannya ialah untuk mencari keuntungan yang sebesar-besarnya. Penelitian ini bertujuan untuk menerapkan data mining dengan metode k-means clustering data ekspor buah-buahan menurut negara tujuannya yang merupakan salah satu komoditas pangan. Penelitian ini menggunakan data pada tahun 2012 sampai 2019 yang diambil melalui situs https://www.bps.go.id. Data diolah dengan mengklasterkan data ekspor kedalam 3 kelompok yaitu tinggi, sedang dan rendah. Didapatkan hasil Centroid data untuk cluster tingkat ekspor tinggi sebesar 2054519.3, centroid data untuk cluster tingkat ekspor sedang sebesar 489020.3, centroid data untuk cluster tingkat ekspor rendah sebesar 20.2. Sehingga diperoleh hasil cluster negara tujuan ekspor yaitu 2 negara cluster tingkat ekspor tinggi yakni negara Tiongkok & Malaysia, 2 negara cluster tingkat ekspor rendah yakni Vietnam & Thailand, dan 6 negara cluster tingkat ekspor rendah yakni Hongkong, Singapura, Nigeria, India, Jepang, Uni Emirat Arab. Informasi pengklasteran data ekspor buah-buahan ini dapat menjadi saran serta masukan bagi pemerintah maupun perusahaan-perusahaan swasta maupun negeri dalam menentukan strategi ekspor buah-buahan dimasa depan. Kata kunci: buah-buahan, data mining, ekspor, k-means, rapid miner. Abstract Export is an economic activity in marketing and selling goods, both industrial, food, and other needs to other countries that have a higher exchange rate or foreign currency value, the aim is to seek the maximum profit. This study discusses the application of data mining using the k-means clustering method on fruit export data based on destination countries. This study uses export data from one of Indonesia's food commodities, namely fruits based on the main destination countries in 2012 to 2019 which was taken through the https://www.bps.go.id site. The data is processed by clustering the export data into 3 groups, namely high, medium and low. The data centroid for the high export level cluster is 2054519.3, the data centroid for the medium export level cluster is 489020.3, the data centroid for the low export level cluster is 20.2. So that an assessment is obtained based on the fruit export index with 2 cluster countries with high export levels, namely China & Malaysia, 2 cluster countries with low export levels, namely Vietnam & Thailand, and 6 cluster countries with low export levels, namely Hong Kong, Singapore, Nigeria, India, Japan, United Arab Emirates. This information on clustering fruit export data can be a suggestion and input for the government and private and state companies in determining fruit export strategies in the future. Keywords: fruits, data mining, exports, k-means, rapid miner.

Milk Quality and Carbon Footprint Indicators of Dairy Sheep Farms Depend on Grazing Level and Identify the Different Management Systems

Currently, there are very few studies in the dairy sheep sector associating milk quality and indicators regarding carbon footprint and their link to grazing levels. For 1 year, monthly milk samples and records related to environmental emissions and management systems were collected through surveys from 17 dairy sheep farms in the region of Castilla y León (Spain), in order to relate this information to the use of natural pastures under free grazing. Indicators were constructed on the collected data and subjected to a multivariate statistical procedure that involved a factor analysis, a cluster analysis and a population canonical analysis. By applying multivariate statistical techniques on milk quality and carbon footprint indicators, it was possible to identify the management system of the farms. From an environmental point of view, farms with a higher grazing level (cluster 4) were more sustainable, as they had the lowest carbon footprint (lower CO2, N2O and CO2 equivalent emissions per sheep and year) and the lowest energy consumption levels, which were gradually lower than those of farms in cluster 3; both indicators were much lower than those of farms in clusters 1 and 2. The milk quality of cluster 1 and 2 farms was significantly lower in terms of total protein and fat content, dry extract, omega-3 fatty acid levels and α-tocopherol content than farms in clusters 3 and 4, which had higher accessibility to grazing resources. In sum, the higher the use of natural resources, the lower the external inputs the farms required and the lower environmental impact and energy costs they have.

Coelimycin Synthesis Activatory Proteins Are Key Regulators of Specialized Metabolism and Precursor Flux in Streptomyces coelicolor A3(2)

Many microbial specialized metabolites are industrially relevant agents but also serve as signaling molecules in intra-species and even inter-kingdom interactions. In the antibiotic-producing Streptomyces, members of the SARP (Streptomyces antibiotic regulatory proteins) family of regulators are often encoded within biosynthetic gene clusters and serve as their direct activators. Coelimycin is the earliest, colored specialized metabolite synthesized in the life cycle of the model organism Streptomyces coelicolor A3(2). Deletion of its two SARP activators cpkO and cpkN abolished coelimycin synthesis and resulted in dramatic changes in the production of the later, stationary-phase antibiotics. The underlying mechanisms of these phenotypes were deregulation of precursor flux and quorum sensing, as shown by label-free, bottom-up shotgun proteomics. Detailed profiling of promoter activities demonstrated that CpkO is the upper-level cluster activator that induces CpkN, while CpkN activates type II thioesterase ScoT, necessary for coelimycin synthesis. What is more, we show that cpkN is regulated by quorum sensing gamma-butyrolactone receptor ScbR.

HierCC: A multi-level clustering scheme for population assignments based on core genome MLST

Motivation Routine infectious disease surveillance is increasingly based on large-scale whole genome sequencing databases. Real-time surveillance would benefit from immediate assignments of each genome assembly to hierarchical population structures. Here we present pHierCC, a pipeline that defines a scalable clustering scheme, HierCC, based on core genome multi-locus typing that allows incremental, static, multi-level cluster assignments of genomes. We also present HCCeval, which identifies optimal thresholds for assigning genomes to cohesive HierCC clusters. HierCC was implemented in EnteroBase in 2018, and has since genotyped >530,000 genomes from Salmonella, Escherichia/Shigella, Streptococcus, Clostridioides, Vibrio and Yersinia. Availability Implementation: https://enterobase.warwick.ac.uk/ and Source codes and instructions: https://github.com/zheminzhou/pHierCC Supplementary information Supplementary data are available at Bioinformatics online.

Abstract P315: Radiomics Signature of DWI-FLAIR Mismatch Correlates With Clinical Phenotype of Patients With Large Vessel Occlusion Stroke

Introduction: For patients presenting with large vessel occlusion for endovascular thrombectomy (EVT), FLAIR hyperintensity within ischemic lesions may be inversely related to parenchymal viability but relies on subjective grading. Radiomics is an emerging image quantification methodology that may more objectively represent continuous image characteristics. We propose a novel radiomics approach to describe infarct FLAIR positivity and evaluate its correlation with clinical presentation. Methods: For patients with pre-EVT MRI, ischemic lesions were manually segmented on DWI, co-registered to FLAIR and visually graded for FLAIR positivity (0, +, ++). Radiomics were extracted within the ischemic lesion outlines on FLAIR. LASSO regression was used to select features for the DWI-FLAIR mismatch radiomics signature. Canonical correlation analysis was used to relate this signature to clinical features. Results: We identified 103 patients with mean age 68±16 years and 63% female. The radiomics signature of DWI-FLAIR mismatch included FLAIR histogram kurtosis and gray level cluster shade; both correlated with visual grading ( Figure 1A ). The first of the estimated 3 canonical pairs was statistically significant (canonical correlation=0.50, corrected p=0.009); kurtosis was positively associated with atrial fibrillation and age, while negatively associated with last known well, smoking, and diabetes ( Figure 1B ). Conclusion: The radiomics signature of DWI-FLAIR mismatch before EVT correlates with visual grading and may provide a continuous metric to describe infarct evolution. Further exploration of larger datasets is required to determine additional granular relationships with clinical features.

Impact of Perceived Behavioral Control on E-learning Adoption

E-learning adoption (AEL) is a contextual phenomenon. Therefore, this research attempted to identify the association of adoption constructs and perceived behavioral control (PBC) on AEL in Sri Lankan private international schools’ context. The sample consisted of students of three leading international school chains. Respondents were selected on multi-level cluster sampling method as per provinces/districts and major cities in which these schools are in operation. Overall, 450 questionnaires were distributed and 271 completed and returned questionnaires (60%) were considered for the final data analysis. A deductive methodology and quantitative method had been followed in the research. PBC factors were identified by conducting PCA. TAM, extended by using the PBC. The empirical evidence supported the positive relationship of adoption variable and PBC variable towards AEL by correlation analysis and hierarchical regression analysis. The presence of differences in AEL between the students engaged in aesthetic subjects and science subject categories were also supported by the contrast test of ANOVA. ANCOVA revealed that the covariate; age was not significantly related to the student’s AEL while the Hayes’s process for moderation analysis has confirmed the relationship between PBC and AEL is being moderated by student’s age.